Our work of tomorrow is going to look vastly different from the work we do today. AI is augmenting workers in certain roles, and soon this trend will expand to the vast majority of the workforce, allowing humans to do the work that matters and, in the process, vastly improving workforce productivity and effectiveness.
But rather than thinking of AI as a superpower in and of itself, it’s essential to view intelligent systems as augmenting human work. The inherent power of AI lies in its ability to deliver the “science” of work while humans complete the “art” of work. This symbiotic relationship is what will define work moving forward.
A new way to approach work
To understand which tasks should be allocated to intelligent machines vs. humans, organizations need to see work not as a broadly defined set of work functions (e.g., research, quality control, product development, etc.) but as a collection of individual activities (i.e., physically connecting wires in a manufacturing process, reporting irregularities to a legal team, logging parts received in a shipment). By analyzing the individual components of job roles, organizations can better assess which are best suited for human workers vs. intelligent systems.
One way to do this is to categorize the functions or tasks within a job role into two buckets, as we did in our previous study “Space Matters: Shaping the Workplace to Get the Right Work Done”:
- Red tasks. Rote, repetitive work with little need for creativity but potentially high amounts of calculation and attention to detail. Because these tasks involve complex data analysis, repetition, pattern recognition and a low level of human interaction, they’re better performed by AI. Think of document scanning, individual parts logging, customer data entry or highly structured risk analysis.
- Blue tasks. Collaborative, social work with high business impact. Because these tasks demand judgment, empathy and creativity, they’re better performed by people than machines. They rely on people gathering together to iterate, experiment, discuss, create and innovate. Think of engagement with an irate customer, corporate networking or exception handling.
Organizations can tune the interplay of humans and machines by applying AI to “red” tasks, thus facilitating workers to better focus on their “blue” tasks. We call this the “task master model.”
Physical task master model
We’ve designed two task master frameworks, one for cognitive and one for physical tasks.
Within the physical task master model, we analyze tasks according to two criteria: the relative social interaction that each entails and the dexterity and structure that each requires.
The best way to understand this is through an example, such as Boeing’s implementation of AR headsets for its wiring loom workers. Traditionally, workers had to refer to massive manuals to reference-check thousands of connections in a wiring loom. The constant toggling between laptop and wiring work caused immense eyestrain and poor productivity.
Boeing introduced AR headsets equipped with natural language processing capabilities. Workers can now ask the headset to describe next possible connections, with the response delivered via an AR interface or hands-on video. This has reduced the production time of wiring looms by 25%.
Seen through the task master model, a low-dexterity, highly structured task that involves no human interaction includes parts logging. This is, therefore, a prime task to be automated. However, the physical connection of wires to the harness, while not requiring any social interaction, does require a high degree of dexterity and, therefore, remains the role of the human worker for the time being. Discussion with managers, colleagues and suppliers, however, is and will remain a human task.